1. Field of the Invention
The present invention generally relates to methods and systems for creating defect classifiers and nuisance filters.
2. Description of the Related Art
The following description and examples are not admitted to be prior art by virtue of their inclusion in this section.
Fabricating semiconductor devices such as logic and memory devices typically includes processing a substrate such as a semiconductor wafer using a large number of semiconductor fabrication processes to form various features and multiple levels of the semiconductor devices. For example, lithography is a semiconductor fabrication process that involves transferring a pattern from a reticle to a resist arranged on a semiconductor wafer. Additional examples of semiconductor fabrication processes include, but are not limited to, chemical-mechanical polishing, etch, deposition, and ion implantation. Multiple semiconductor devices may be fabricated in an arrangement on a single semiconductor wafer and then separated into individual semiconductor devices.
Inspection processes are used at various steps during a semiconductor manufacturing process to detect defects on wafers. Inspection processes have always been an important part of fabricating semiconductor devices such as integrated circuits. However, as the dimensions of semiconductor devices decrease, inspection processes become even more important to the successful manufacture of acceptable semiconductor devices. For instance, as the dimensions of semiconductor devices decrease, detection of defects of decreasing size has become necessary since even relatively small defects may cause unwanted aberrations in the semiconductor devices.
Automatic defect classification (ADC) of semiconductor defects is an important application of wafer inspection and defect review tools. The most popular and most trusted defect classifiers and nuisance filters used during wafer inspection are manually created decision trees. By far, the most common method for creating defect classification trees is a manual approach with several ease-of-use features such as the ability to copy and paste sub-trees, etc. The success of these methods depends not only on the experience and patience of the engineer, but also on the time available for this task. It is quite clear that this approach is and always will be subjective, error-prone, and subject to the limited capabilities of humans to explore relatively large spaces of defect properties at each important classifier node.
Some alternative approaches to classifier creation not widely adopted today involve some degree of automation. Generally, these methods require classified defects with designations of nuisance, real, and defects of interest (DOI). The goal of these methods may be to produce tuned classifiers, or multiple classifier candidates, as a starting point for manual tuning and adjustments.
The currently used methods described above have a number of important disadvantages. For example, the disadvantages of the manual approach are its: (a) subjectivity; (b) reliance on the skills and experience of human experts; (c) dependence on the amount of time available for the classifier creation (which can vary widely); and (d) propensity to human-induced errors (through repetition and mental exhaustion).
A disadvantage of the “automated” approaches is that they construct and tune classifiers in one step by trying to identify the best discrimination criteria between defect types (manually classified defects) at each node of the classifier. In addition, they ignore the well-established practice of two-step classifier creation: (1) first, create a decision tree that separates defects into stable populations (by region, polarity, and other segmentation attributes); and (2) tune these populations independently of each other. Furthermore, some algorithms used in such methods may be completely unconstrained. They may create classifiers that do not adhere to the principle of separation and tuning in two steps. Modification of the decision trees created by these methods is cumbersome. Other algorithms used in these methods have more flexibility if they can be run from any leaf node of a sub-tree. As such, they may be applied to the separated populations for the final step of classifier tuning. However, this mode of operation still requires people to do the entire initial classifier creation manually including the search for separable populations. The algorithm only helps with the tuning part of decision tree creation.
The reality is that classifiers are constructed on training data sets first without knowing the defect types with a high degree of certainty. As mentioned above, the guiding principle is to construct a classifier that separates defects into stable populations (segmentation by clustering, etc.), then sample defects from the leaf nodes to allow tuning, and finally tune the leaf nodes based on the defect types.
Accordingly, it would be advantageous to develop systems and methods for setting up defect classifiers for wafer inspection that address the shortcomings of the methods described above.